Personalized control system and method for intelligent exercise equipment

Abstract

The invention discloses an intelligent exercise equipment personalized control system and method, and relates to the technical field of exercise equipment control, and the method comprises the steps: evaluating the sign approximation degree between other users and users, and obtaining an approximate user; acquiring historical sign change records and historical exercise records of the approximate users, acquiring initial historical exercise records and target sign data of the users, and analyzing exercise reference values of the approximate users to the users to obtain exercise reference users; historical equipment operation records generated in the using process of the intelligent exercise equipment and the exercise reference user are obtained, the influence degree of the intelligent exercise equipment on the exercise effect of the user in different operation states is analyzed, and target operation data are obtained; when the user uses the intelligent exercise equipment to exercise, the operation state of the intelligent exercise equipment is controlled according to the target operation data, and message prompting is carried out on the user.

Description

Technical Field

[0001] The present invention relates to the technical field of exercise equipment control, and specifically to an intelligent exercise equipment personalized control system and method. Background Art

[0002] With the continuous development of the intelligence of exercise equipment, more and more people will use intelligent exercise equipment for exercise. There are the following advantages of using intelligent exercise equipment: 1. Real-time monitoring. Using intelligent exercise equipment can monitor heart rate and calories in real time, allowing users to understand their physical status in real time; 2. Diversified exercise. Multiple exercise modes and types are stored in intelligent exercise equipment, and users can adjust according to their own preferences and needs to ensure the effectiveness and freshness of exercise; 3. High safety. By monitoring and feedback of users' physical status, the risk of injury caused by improper techniques or excessive exercise can be effectively reduced.

[0003] Common intelligent exercise equipment on the market currently mainly monitors physical data such as calories consumed by users during exercise, and then recommends exercise modes to users according to their exercise plans. However, in actual situations, the physical status of different users is different, and the exercise effects of the same exercise intensity on different users will also be different. Moreover, the exercise goals of different users are also different. There are few intelligent exercise equipment that can formulate personalized exercise strategies for the equipment and the users themselves to control the exercise equipment, which will not only reduce the exercise effects of users, but may even cause users to get injured during exercise. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent exercise equipment personalized control system and method to solve the problems raised in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: An intelligent exercise equipment personalized control method, the method includes:

[0006] Step S100: Obtain the initial physical signs data of the user using the intelligent exercise equipment, obtain the historical initial physical signs data of other users using the intelligent exercise equipment, evaluate the similarity degree of physical signs between other users and the user, and obtain similar users;

[0007] Step S200: Obtain the historical physical signs change records and historical exercise records of the similar users, obtain the initial historical exercise records and target physical signs data of the user, analyze the exercise reference value of the similar users to the user, and obtain exercise reference users;

[0008] Step S300: Obtain the intelligent exercise equipment, analyze the historical equipment operation records generated during the use by the exercise reference users, and analyze the influence degree of the intelligent exercise equipment on the user's exercise effect in different operation states to obtain the target operation data;

[0009] Step S400: When the user uses the intelligent exercise equipment for exercise, control the operation state of the intelligent exercise equipment according to the target operation data, and give a message prompt to the user.

[0010] Further, step S100 includes:

[0011] Step S101: Obtain the user's initial physical sign data, where the initial physical sign data includes the values of each physical sign index of the user when starting to use the intelligent exercise equipment;

[0012] Step S102: Obtain the historical initial physical sign data of other users who use the intelligent exercise equipment. Among them, other users use the intelligent exercise equipment for exercise within the historical period, and obtain the values of each physical sign index of other users when starting to use the intelligent exercise equipment from the initial historical feature data;

[0013] Step S103: Obtain each other user who uses the intelligent exercise equipment, and calculate the characteristic value a=(a △ -a′) / σ of the physical sign index of the user in the initial feature data, where σ is the standard deviation of the physical sign indexes of each other user when starting to use the intelligent exercise equipment, a △ is the value of the physical sign index of the user in the initial feature data, and a′ is the average value of the physical sign indexes of each other user when starting to use the intelligent exercise equipment;

[0014] Step S104: Obtain the characteristic values of each physical sign index of the user in the initial feature data, and aggregate them to obtain the user's initial feature vector A={a1, a2,..., a n}, where a1, a2,..., a n are the characteristic values of the 1st, 2nd,..., nth physical sign indexes of the user respectively;

[0015] Step S105: Evaluate the physical sign approximation degree between each other user and the user. Among them, the specific process of evaluating the physical sign approximation degree between the cth other user and the user is as follows:

[0016] Calculate the physical sign approximation value r c :

[0017]

[0018] Among them, B cis the initial feature vector of the c-th other user;

[0019] When the physical sign approximation value r c is greater than the preset feature approximation threshold, it is determined that the physical signs of the c-th other user and the user are approximated, and the c-th other user is recorded as the approximate user of the user;

[0020] The reason for calculating the eigenvalue of the physical sign index of the user in the above steps is that in the actual process, the numerical value ranges and units of different item feature indexes are different. By calculating the eigenvalue of the feature index, the calculated physical sign approximation value can be made more accurate, providing strong data support for the control of the intelligent exercise equipment in the following text.

[0021] Further, step S200 includes:

[0022] Step S201: Obtain each approximate user of the user, record the physical sign states of the approximate users after exercising with the intelligent exercise equipment, obtain the historical feature change records of the approximate users, and extract the values of each physical sign index of the approximate users after exercise from the historical feature change records;

[0023] Step S202: Obtain the target physical sign data of the user. The target physical sign data includes the target values of each physical sign index of the user. Obtain each historical feature change record of the approximate user, and calculate the target physical sign target values of the approximate users in each historical feature change record for the user. Among them, the physical sign target value F of the approximate user in the e-th historical feature change record for the user e :

[0024]

[0025] where n is the total number of physical sign indexes of the user; a i,target is the target value of the i-th physical sign index of the user; d e,i are the values of each physical sign index of the approximate user after exercise in the e-th historical feature change record;

[0026] Step S203: Obtain the maximum value of the physical sign target values of the approximate users for the user in each historical feature change record, and record it as the target physical sign target value between the approximate user and the user, and perform normalization processing on the target physical sign target value;

[0027] Step S204: Obtain the historical exercise records of the approximate users, and obtain the values of each exercise index of the approximate users when using the intelligent exercise equipment for exercise from the historical exercise records;

[0028] Record the exercise process of the user using the intelligent exercise equipment within the historical period to obtain the user's initial historical exercise record. Obtain the total number g of the user's initial historical exercise records, obtain approximately the first g historical exercise records of the user, and record the time point at which the (g - 1)-th historical exercise record is located as the characteristic time point;

[0029] Step S205: Obtain the values of various exercise indicators when the user uses the intelligent exercise equipment for exercise from the initial historical exercise record, and analyze the exercise reference value of the approximate user to the user. The specific analysis process is as follows:

[0030] Calculate the similarity degree H of the operation indicators between the approximate user and the user:

[0031]

[0032] where Q x is the value of the operation indicator in the x-th historical exercise record of the approximate user; Q' is the average value of the operation indicators in the first g historical exercise records of the approximate user; W x is the value of the operation indicator in the x-th initial historical exercise record of the user; W' is the average value of the operation indicators in each initial historical exercise record of the approximate user;

[0033] Calculate the exercise reference score K of the approximate user to the user:

[0034]

[0035] where m is the total number of the user's various exercise indicators; η1 and η2 are respectively the preset first scoring coefficient and second scoring coefficient, η1 + η2 = 1, η1 > 0, η2 > 0; F' max is the target physical sign target value after normalization between the approximate user and the user; H z is the similarity degree of the z-th operation indicator between the approximate user and the user;

[0036] Step S206: When the exercise reference score K is greater than the preset reference score threshold, determine that the approximate user has reference value for the user's exercise, record the approximate user as the exercise reference user of the user, and obtain each exercise reference user of the user.

[0037] Further, step S300 includes:

[0038] Step S301: Obtain the historical device operation record generated during the use of the intelligent exercise equipment by the exercise reference user, set the unit time length, obtain the average value of each operation indicator of the intelligent exercise equipment within each unit time length from the historical device operation record, and collect them to obtain the operation data set of each operation indicator;

[0039] Step S302: Analyze the impact degree of the intelligent exercise equipment on the user's exercise effect in different operating states. The specific analysis process is as follows:

[0040] Obtain the first historical device operation record of the exercise reference user after the characteristic time point, and record it as the characteristic historical device operation record;

[0041] Obtain the mean value of each element in the operation data set of each operation index in the corresponding characteristic historical device operation record of each exercise reference user of the user, and record it as the target value of each operation index in the intelligent exercise equipment within each unit time period after the user starts exercising;

[0042] Step S303: Calculate the characteristic change rate p of a certain operation index in the intelligent exercise equipment within the vth unit time period after the user starts exercising v =(L v -L v-1 ) / L v-1 , where L v-1 is the target value of a certain operation index within the (v - 1)th unit time period after the user starts exercising, and L v is the target value of a certain operation index within the vth unit time period after the user starts exercising;

[0043] When the characteristic change rate p v is less than the preset change rate threshold, use the value of L v-1 to replace the value of L v . Determine that when the operation indexes of the intelligent exercise equipment are the target values, the exercise effect on the user is the best. Replace and collect the target values of the operation indexes in the intelligent exercise equipment within each unit time period after the user starts exercising to obtain the target operation data.

[0044] Furthermore, step S400 includes:

[0045] Step S401: Obtain the target operation data of the user. When the user starts exercising using the intelligent exercise equipment in the current cycle, based on the target operation data, adjust the operation indexes of the intelligent exercise equipment, control the operation state of the intelligent exercise equipment, and send a prompt to the user;

[0046] Step S402: After the user completes the exercise using the intelligent exercise equipment in the current cycle, obtain each exercise reference user, and obtain the mean value of each element in the operation data set of each operation index in the second historical equipment operation record after the characteristic time point, and generate the target operation data for the next cycle of the user in the current cycle. By analogy, obtain the target operation data for each cycle after the current cycle of the user, and control the intelligent exercise equipment when the user uses the intelligent exercise equipment for exercise.

[0047] In order to better implement the above method, an intelligent exercise equipment personalized control system is also proposed. The system includes a physical sign approximation evaluation module, an exercise reference analysis module, a target operation data module, and an operation control module;

[0048] The physical sign approximation evaluation module is used to evaluate the physical sign approximation degree between other users and the user to obtain approximate users;

[0049] The exercise reference analysis module is used to analyze the exercise reference value of approximate users to the user to obtain exercise reference users;

[0050] The target operation data module is used to analyze the influence degree of the intelligent exercise equipment in different operation states on the user's exercise effect to obtain target operation data;

[0051] The operation control module is used to control the operation state of the intelligent exercise equipment according to the target operation data and give a message prompt to the user.

[0052] Further, the physical sign approximation evaluation module includes a physical sign approximation value unit and a physical sign approximation evaluation unit;

[0053] The physical sign approximation value unit is used to calculate the physical sign approximation value between each other user and the user;

[0054] The physical sign approximation evaluation unit is used to evaluate the physical sign approximation degree between each other user and the user according to the physical sign approximation value to obtain approximate users.

[0055] Further, the exercise reference analysis module includes an exercise reference scoring unit and an exercise reference analysis unit;

[0056] The exercise reference scoring unit is used to calculate the exercise reference score of approximate users to the user;

[0057] The exercise reference analysis unit is used to analyze the exercise reference value of approximate users to the user according to the exercise reference score to obtain exercise reference users.

[0058] Further, the target operation data module includes an operation data set unit and a target operation data unit;

[0059] An operating dataset unit, configured to obtain the historical device operation records of exercise reference users, and from the historical device operation records, obtain the average values of various operation indicators of the intelligent exercise equipment within each unit time period, and perform aggregation to obtain an operation dataset of various operation indicators;

[0060] A target operation data unit, configured to analyze the influence degree of the intelligent exercise equipment on the user's exercise effect in different operation states according to the operation dataset, and obtain target operation data.

[0061] Furthermore, the operation control module includes an operation control unit;

[0062] The operation control unit is configured to adjust various operation indicators of the intelligent exercise equipment according to the target operation data, control the operation state of the intelligent exercise equipment, and issue a prompt to the user.

[0063] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention realizes the personalized control of intelligent exercise equipment. Considering that in the actual process, the physical states of different users are different, the exercise effects of the same exercise intensity on different users are also different, and the exercise goals of different users are also different. Starting from multiple angles such as the exercise goals, physical characteristics, and exercise characteristics of users, the optimal operation data of the intelligent exercise equipment is formulated to control the intelligent exercise equipment, which not only reduces the physical damage of users during exercise, but also can greatly improve the exercise effect of users. Brief Description of the Drawings

[0064] Figure 1 is a method flow chart of a personalized control method for an intelligent exercise equipment of the present invention;

[0065] Figure 2 is a module schematic diagram of a personalized control system for an intelligent exercise equipment of the present invention. Detailed Embodiments

[0066] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0067] Embodiment: As Figure 1 - Figure 2 shown, the present invention provides a technical solution, a personalized control method for an intelligent exercise equipment, the method includes:

[0068] Step S100: Obtain the initial physical sign data of the user using the intelligent exercise equipment, obtain the historical initial physical sign data of other users using the intelligent exercise equipment, evaluate the similarity degree of physical signs between other users and the user, and obtain similar users;

[0069] Among them, step S100 includes:

[0070] Step S101: Obtain the initial physical sign data of the user. The initial physical sign data includes the values of various physical sign indicators of the user when starting to use the intelligent exercise equipment;

[0071] For example, the various characteristic indicators include weight, muscle content, etc.;

[0072] Step S102: Obtain the historical initial physical sign data of other users using the intelligent exercise equipment. Among them, other users use the intelligent exercise equipment for exercise within the historical period. From the initial historical characteristic data, obtain the values of various physical sign indicators of other users when starting to use the intelligent exercise equipment;

[0073] Step S103: Obtain each other user using the intelligent exercise equipment, and calculate the characteristic value a=(a △ -a′) / σ of the physical sign indicators of the user in the initial characteristic data, where σ is the standard deviation of the physical sign indicators of each other user when starting to use the intelligent exercise equipment, a △ is the value of the physical sign indicator of the user in the initial characteristic data, and a′ is the average value of the physical sign indicators of each other user when starting to use the intelligent exercise equipment;

[0074] Step S104: Obtain the characteristic values of the various physical sign indicators of the user in the initial characteristic data, and perform aggregation to obtain the initial characteristic vector A={a1, a2,..., a n} of the user, where a1, a2,..., a n are the characteristic values of the 1st, 2nd,..., nth physical sign indicators of the user respectively;

[0075] Step S105: Evaluate the similarity degree of physical signs between each other user and the user. Among them, to evaluate the similarity degree of physical signs between the cth other user and the user, the specific process is:

[0076] Calculate the physical sign similarity value r c :

[0077]

[0078] Among them, B c is the initial characteristic vector of the cth other user;

[0079] When the physical sign similarity value rc If it is greater than a preset feature approximation threshold, it is determined that the physical signs between the c-th other user and the user are approximated, and the c-th other user is recorded as the approximate user of the user;

[0080] Step S200: Obtain the historical physical sign change records and historical exercise records of the approximate users, obtain the initial historical exercise records and target physical sign data of the user, analyze the exercise reference value of the approximate users to the user, and obtain the exercise reference users;

[0081] Among them, step S200 includes:

[0082] Step S201: Obtain each approximate user of the user, record the physical sign states of the approximate users after exercising with intelligent exercise equipment, obtain the historical feature change records of the approximate users, and extract the values of each physical sign index of the approximate users after exercise from the historical feature change records;

[0083] Step S202: Obtain the target physical sign data of the user. The target physical sign data includes the target values of each physical sign index of the user. Obtain each historical feature change record of the approximate user, and calculate the target physical sign target values of the approximate users in each historical feature change record for the user. Among them, the physical sign target value F of the approximate user in the e-th historical feature change record for the user e :

[0084]

[0085] Among them, n is the total number of physical sign indexes of the user; a i,target is the target value of the i-th physical sign index of the user; d e,i are the values of each physical sign index of the approximate user after exercise in the e-th historical feature change record;

[0086] Step S203: Obtain the maximum value of the physical sign target values of the approximate users for the user in each historical feature change record, and record it as the target physical sign target value between the approximate user and the user, and perform normalization processing on the target physical sign target value;

[0087] Step S204: Obtain the historical exercise records of the approximate users, and obtain the values of each exercise index of the approximate users when using intelligent exercise equipment for exercise from the historical exercise records;

[0088] For example, each exercise index includes the heart rate and calorie consumption of the human body;

[0089] Record the exercise process of the user using the intelligent exercise equipment within the historical period to obtain the user's initial historical exercise record. Obtain the total number g of the user's initial historical exercise records, obtain approximately the first g historical exercise records of the user, and record the time point at which the (g - 1)-th historical exercise record is located as the characteristic time point;

[0090] Step S205: Obtain the values of various exercise indicators when the user uses the intelligent exercise equipment for exercise from the initial historical exercise record, and analyze the exercise reference value of the approximate user to the user. The specific analysis process is as follows:

[0091] Calculate the similarity degree H of the exercise indicators between the approximate user and the user:

[0092]

[0093] where Q x is the value of the exercise indicator in the x-th historical exercise record of the approximate user; Q′ is the average value of the exercise indicators in the first g historical exercise records of the approximate user; W x is the value of the exercise indicator in the x-th initial historical exercise record of the user; W′ is the average value of the exercise indicators in each initial historical exercise record of the approximate user;

[0094] Calculate the exercise reference score K of the approximate user to the user:

[0095]

[0096] where m is the total number of the user's various exercise indicators; η1 and η2 are respectively the preset first scoring coefficient and second scoring coefficient, η1 + η2 = 1, η1 > 0, η2 > 0; F′ max is the target physical sign target value after normalization between the approximate user and the user; H z is the similarity degree of the z-th exercise indicator between the approximate user and the user;

[0097] For example, m is 3; F′ max is 0.80; η1 is 0.4; η2 is 0.6; H1 is 0.70; H2 is 0.80; H3 is 0.90;

[0098] Calculate the exercise reference score K of the approximate user to the user:

[0099]

[0100] Step S206: When the exercise reference score K is greater than the preset reference score threshold, determine that the approximate user has reference value for the user's exercise, record the approximate user as the exercise reference user of the user, and obtain each exercise reference user of the user;

[0101] Step S300: Obtain an intelligent exercise equipment, analyze the historical equipment operation records generated during the use by the exercise reference user, and analyze the influence degree of the intelligent exercise equipment on the user's exercise effect in different operation states to obtain target operation data;

[0102] Among them, step S300 includes:

[0103] Step S301: Obtain an intelligent exercise equipment, analyze the historical equipment operation records generated during the use by the exercise reference user, set a unit time period, obtain the average value of each operation index of the intelligent exercise equipment within each unit time period from the historical equipment operation records, and collect them to obtain an operation data set of each operation index;

[0104] For example, each operation index includes power output, operation speed, etc.;

[0105] Step S302: Analyze the influence degree of the intelligent exercise equipment on the user's exercise effect in different operation states. The specific analysis process is as follows:

[0106] Obtain the first historical equipment operation record after the characteristic time point of the exercise reference user, and record it as the characteristic historical equipment operation record;

[0107] Obtain the average value of each element in the operation data set of each operation index in the corresponding characteristic historical equipment operation record of each exercise reference user, and record it as the target value of each operation index in the intelligent exercise equipment within each unit time period after the user starts exercising;

[0108] Step S303: Calculate the characteristic change rate p of a certain operation index in the intelligent exercise equipment within the v-th unit time period after the user starts exercising v =(L v -L v-1 ) / L v-1 , where L v-1 is the target value of a certain operation index within the (v - 1)-th unit time period after the user starts exercising, and L v is the target value of a certain operation index within the v-th unit time period after the user starts exercising;

[0109] When the characteristic change rate p v is less than the preset change rate threshold, use the value of L v-1 to replace the value of L v . Determine that when the operation indexes in the intelligent exercise equipment are the target values, the exercise effect on the user is the best. Replace the target values of the operation indexes in the intelligent exercise equipment within each unit time period after the user starts exercising, and collect them to obtain the target operation data;

[0110] Step S400: When the user exercises using the intelligent exercise equipment, control the operating state of the intelligent exercise equipment according to the target operation data, and give a message prompt to the user;

[0111] Among them, step S400 includes:

[0112] Step S401: Obtain the target operation data of the user. When the user starts exercising using the intelligent exercise equipment in the current cycle, based on the target operation data, adjust the various operation indicators of the intelligent exercise equipment, control the operating state of the intelligent exercise equipment, and give a prompt to the user;

[0113] Step S402: When the user finishes exercising using the intelligent exercise equipment in the current cycle, obtain each exercise reference user, and calculate the mean value of each element in the operation data set of the various operation indicators in the second historical equipment operation record after the characteristic time point, and generate the target operation data of the user in the next cycle of the current cycle, and so on, obtain the target operation data of the user in each cycle after the current cycle, and control the intelligent exercise equipment when the user exercises using the intelligent exercise equipment;

[0114] In order to better implement the above method, an intelligent exercise equipment personalized control system is also proposed. The system includes a physical sign approximation evaluation module, an exercise reference analysis module, a target operation data module, and an operation control module;

[0115] The physical sign approximation evaluation module is used to evaluate the physical sign approximation degree between other users and the user to obtain approximate users;

[0116] The exercise reference analysis module is used to analyze the exercise reference value of the approximate users to the user to obtain exercise reference users;

[0117] The target operation data module is used to analyze the influence degree of the intelligent exercise equipment in different operating states on the user's exercise effect to obtain target operation data;

[0118] The operation control module is used to control the operating state of the intelligent exercise equipment according to the target operation data and give a message prompt to the user;

[0119] Among them, the physical sign approximation evaluation module includes a physical sign approximation value unit and a physical sign approximation evaluation unit;

[0120] The physical sign approximation value unit is used to calculate the physical sign approximation values between each other user and the user;

[0121] The physical sign approximation evaluation unit is used to evaluate the physical sign approximation degree between each other user and the user according to the physical sign approximation values to obtain approximate users;

[0122] Among them, the exercise reference analysis module includes an exercise reference scoring unit and an exercise reference analysis unit;

[0123] The exercise reference scoring unit is used to calculate the exercise reference score of the approximate user for the user.

[0124] The exercise reference analysis unit is used to analyze the exercise reference value of the approximate user for the user according to the exercise reference score, and obtain the exercise reference user.

[0125] Among them, the target operation data module includes an operation data set unit and a target operation data unit;

[0126] The operation data set unit is used to obtain the historical device operation records of the exercise reference user, and from the historical device operation records, obtain the average values of various operation indicators of the intelligent exercise equipment within each unit time period, and gather them to obtain an operation data set of various operation indicators.

[0127] The target operation data unit is used to analyze the influence degree of the intelligent exercise equipment on the user's exercise effect under different operation states according to the operation data set, and obtain the target operation data.

[0128] Among them, the operation control module includes an operation control unit;

[0129] The operation control unit is used to adjust the various operation indicators of the intelligent exercise equipment according to the target operation data, control the operation state of the intelligent exercise equipment, and give a prompt to the user.

[0130] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. An intelligent exercise equipment personalized control method, characterized in that, The method includes: Step S100: Obtain the initial physical sign data of the user using the intelligent exercise equipment, obtain the historical initial physical sign data of other users using the intelligent exercise equipment, evaluate the similarity degree of physical signs between the other users and the user, and obtain similar users; Step S200: Obtain the historical physical sign change records and historical exercise records of the similar users, obtain the initial historical exercise records and target physical sign data of the user, analyze the exercise reference value of the similar users to the user, and obtain exercise reference users; Step S300: Obtain the intelligent exercise equipment, analyze the historical equipment operation records generated during the use of the intelligent exercise equipment by the exercise reference users, and analyze the influence degree of the intelligent exercise equipment in different operation states on the exercise effect of the user to obtain target operation data; Step S400: When the user uses the intelligent exercise equipment for exercise, control the operation state of the intelligent exercise equipment according to the target operation data, and give a message prompt to the user.

2. The personalized control method for an intelligent exercise equipment according to claim 1, wherein The step S100 includes: Step S101: Obtain the initial physical sign data of the user, where the initial physical sign data includes the values of various physical sign indicators of the user when starting to use the intelligent exercise equipment; Step S102: Obtain the historical initial physical sign data of other users using the intelligent exercise equipment. Among them, the other users use the intelligent exercise equipment for exercise within a historical period, and obtain the values of various physical sign indicators of the other users when starting to use the intelligent exercise equipment from the initial historical feature data; Step S103: Obtain each other user who uses the intelligent exercise equipment, and calculate the eigenvalue a = (a △ - a') / σ of the physical sign index of the user in the initial feature data, where σ is the standard deviation of the physical sign index of each other user when starting to use the intelligent exercise equipment, and a △ is the value of the physical sign index of the user in the initial feature data, and a' is the average value of the physical sign index of each other user when starting to use the intelligent exercise equipment; Step S104: Obtain the eigenvalue of each physical sign index of the user in the initial feature data, and perform aggregation to obtain the initial feature vector A of the user = {a1, a2,..., a n}, where a1, a2,..., a n are the eigenvalues of the 1st, 2nd,..., nth physical sign indexes of the user respectively; Step S105: Evaluate the similarity degree of physical signs between each of the other users and the user. Among them, the specific process of evaluating the similarity degree of physical signs between the c-th other user and the user is as follows: Calculate the physical sign approximation value r between the c-th other user and the user c : Among them, B c is the initial feature vector of the c-th other user; When the physical sign approximation value r c is greater than a preset feature approximation threshold, it is determined that the physical signs of the c-th other user and the user are approximated, and the c-th other user is recorded as the approximate user of the user.

3. The personalized control method for an intelligent exercise equipment according to claim 2, wherein, The step S200 includes: Step S201: Obtain each similar user of the user, record the physical sign states of the similar users after using the intelligent exercise equipment for exercise, obtain the historical feature change records of the similar users, and extract the values of various physical sign indicators of the similar users after exercise from the historical feature change records; Step S202: Obtain the target physical sign data of the user, where the target physical sign data includes the target values of various physical sign indicators of the user. Obtain the respective historical feature change records of the approximate user, and calculate the target physical sign target values of the approximate user for the user in the respective historical feature change records. Among them, the physical sign target value F of the approximate user for the user in the e-th historical feature change record e : where n is the total number of the user's physical sign indicators; a i,target is the target value of the i-th physical sign indicator of the user; d e,i is the value of each physical sign indicator after the approximate user exercises in the e-th historical feature change record; Step S203: Obtain the maximum value of the physical sign target values of the similar users to the user in each of the historical feature change records, and record it as the target physical sign target value between the similar users and the user, and perform normalization processing on the target physical sign target value; Step S204: Obtain the historical exercise records of the similar users, and obtain the values of various exercise indicators of the similar users when using the intelligent exercise equipment for exercise from the historical exercise records; Record the exercise process of the user using the intelligent exercise equipment within a historical period to obtain the initial historical exercise records of the user, obtain the total number g of the initial historical exercise records of the user, obtain the first g historical exercise records of the similar users, and record the time point where the (g - 1)-th historical exercise record is located as the feature time point; Step S205: Obtain the values of various exercise indicators when the user exercises using the intelligent exercise equipment from the initial historical exercise record, and analyze the exercise reference value of the approximate user for the user. The specific analysis process is as follows: Calculate the similarity degree H of the exercise indicators between the approximate user and the user: Among them, Q x is the value of the running index in the x-th historical exercise record of the approximate user; Q' is the average value of the running index in the first g historical exercise records of the approximate user; W x is the value of the running index in the x-th initial historical exercise record of the user; W' is the average value of the running index in each initial historical exercise record of the approximate user; Calculate the exercise reference score K of the approximate user for the user: where m is the total number of all the exercise indexes of the user; η1 and η2 are respectively a preset first scoring coefficient and a second scoring coefficient, η1 + η2 = 1, η1 > 0, η2 > 0; F′ max is the target physical sign target value after normalization between the approximate user and the user; H z is the similarity degree of the z-th running index between the approximate user and the user; Step S206: When the exercise reference score K is greater than the preset reference score threshold, determine that the approximate user has reference value for the user's exercise, record the approximate user as the exercise reference user of the user, and obtain each exercise reference user of the user.

4. The personalized control method for an intelligent exercise equipment according to claim 3, wherein The step S300 includes: Step S301: Obtain the historical device operation record generated during the use of the intelligent exercise equipment by the exercise reference user. Set the unit time length, and obtain the average value of each operation indicator of the intelligent exercise equipment within each unit time length from the historical device operation record, and perform aggregation to obtain the operation data set of each operation indicator; Step S302: Analyze the influence degree of the intelligent exercise equipment in different operation states on the user's exercise effect. The specific analysis process is as follows: Obtain the first historical device operation record after the characteristic time point of the exercise reference user, and record it as the characteristic historical device operation record; Obtain the mean value of each element in the operation data set of each operation indicator in the corresponding characteristic historical device operation record of each exercise reference user of the user, and record it as the target value of each operation indicator in the intelligent exercise equipment within each unit time length after the user starts exercising; Step S303: Calculate the characteristic change rate p of a certain operation index in the intelligent exercise equipment within the v-th unit time period after the user starts exercising v =(L v -L v-1 ) / L v-1 , where L v-1 is the target value of the certain operation index within the (v - 1)-th unit time period after the user starts exercising, and L v is the target value of the certain operation index within the v-th unit time period after the user starts exercising; When the feature change rate p v is less than a preset change rate threshold, use the value of L v-1 to replace the value of L v value, and determine that when the operating indicators in the intelligent exercise equipment are target values, the exercise effect on the user is the best. Replace the target values of the operating indicators in the intelligent exercise equipment for each unit time period after the user starts exercising, and collect them to obtain target operation data.

5. The personalized control method of an intelligent exercise equipment according to claim 4, characterized in that The step S400 includes: Step S401: Obtain the target operation data of the user. When the user starts exercising using the intelligent exercise equipment in the current cycle, based on the target operation data, adjust the operation indicators of the intelligent exercise equipment, control the operation state of the intelligent exercise equipment, and send a prompt to the user; Step S402: When the user completes the exercise using the intelligent exercise equipment in the current cycle, obtain the mean value of each element in the operation data set of each operation indicator in the second historical device operation record after the characteristic time point of each exercise reference user, and generate the target operation data of the user in the next cycle of the current cycle, and so on, obtain the target operation data of the user in each cycle after the current cycle, and control the intelligent exercise equipment when the user exercises using the intelligent exercise equipment.

6. An intelligent exercise equipment personalized control system for implementing the intelligent exercise equipment personalized control method according to any one of claims 1-5, characterized in that, The system includes a physical sign approximation evaluation module, an exercise reference analysis module, a target operation data module, and an operation control module; The physical sign approximation evaluation module is used to evaluate the physical sign approximation degree between the other user and the user to obtain an approximate user; The exercise reference analysis module is used to analyze the exercise reference value of the approximate user for the user to obtain an exercise reference user; The target operation data module is used to analyze the influence degree of the intelligent exercise equipment on the user's exercise effect in different operation states, and obtain the target operation data; The operation control module is used to control the operation state of the intelligent exercise equipment according to the target operation data, and give message prompts to the user.

7. The personalized control system of an intelligent exercise equipment according to claim 6, characterized in that The physical sign approximation evaluation module includes a physical sign approximation value unit and a physical sign approximation evaluation unit; The physical sign approximation value unit is used to calculate the physical sign approximation values between each other user and the user; The physical sign approximation evaluation unit is used to evaluate the physical sign approximation degree between each other user and the user according to the physical sign approximation value, and obtain the approximate users.

8. The personalized control system of an intelligent exercise equipment according to claim 6, characterized in that, The exercise reference analysis module includes an exercise reference scoring unit and an exercise reference analysis unit; The exercise reference scoring unit is used to calculate the exercise reference scores of the approximate users for the user; The exercise reference analysis unit is used to analyze the exercise reference value of the approximate users for the user according to the exercise reference score, and obtain the exercise reference users.

9. The personalized control system of an intelligent exercise equipment according to claim 6, characterized in that, The target operation data module includes an operation data set unit and a target operation data unit; The operation data set unit is used to obtain the historical device operation records of the exercise reference users, and from the historical device operation records, obtain the average values of the operation indicators of the intelligent exercise equipment in each unit time period, and collect them to obtain the operation data set of the operation indicators; The target operation data unit is used to analyze the influence degree of the intelligent exercise equipment on the user's exercise effect in different operation states according to the operation data set, and obtain the target operation data.

10. The personalized control system of an intelligent exercise equipment according to claim 6, characterized in that, The operation control module includes an operation control unit; The operation control unit is used to adjust the operation indicators of the intelligent exercise equipment according to the target operation data, control the operation state of the intelligent exercise equipment, and give prompts to the user.

Images